Open AccessHanging protocol optimization of lumbar spine radiographs with machine learning
Author(s) -
Gene Kitamura
Publication year - 2021
Publication title -
skeletal radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.571
H-Index - 91
eISSN - 1432-2161
pISSN - 0364-2348
DOI - 10.1007/s00256-021-03733-8
Subject(s) - rotation (mathematics) , medicine , radiography , oblique case , lumbar , artificial intelligence , position (finance) , categorical variable , orthodontics , nuclear medicine , computer science , anatomy , radiology , machine learning , linguistics , philosophy , finance , economics
The purpose of this study was to determine whether machine learning algorithms can be utilized to optimize the hanging protocol of lumbar spine radiographs. Specifically, we explored whether machine learning models can accurately label lumbar spine views/positions, detect hardware, and rotate the lateral views to straighten the image.